1. Integration of Representation Into Goal- Driven Behavior-Based Robots By Dr. Maja J. Mataric` Presented by Andy Klempau

2. Introduction Hybrid system separates reactive low level actions from deliberative decision actions. Alternative to Hybrid approach is Behavior- Based system. Behavior-Based system combines a reactive subsumption foundation with a decision maker. Toto is a Behavior-Based robot that explores dynamic office environments, identifies landmarks, maps the landmarks, and uses the map for path planning.

3. Outline Toto’s Structure Basic Navigation ( Exploring ) Landmark Detection Mapping Landmarks Path Planning

4. Toto’s Structure Cylindrical Robot 12 Sonar Sensors 4 Bit Compass (16 states)

5. Basic Navigation Subsumption Architecture Highest priority is Stroll behavior Lowest priority is Correct behavior

6. Basic Navigation Stroll : if( min( sonars 1 2 3 4 ) <= danger-zone ) if( not stopped ) stop else move backward else move forward

7. Basic Navigation Avoid : if( ( sonar 3 or 4 ) <= safe-zone ) turn left else if( ( sonar 1 or 2 ) <= safe-zone ) turn right

8. Basic Navigation Align : if( ( sonar 7 or 8 ) edging-distance ) turn right if( ( sonar 9 or 10 ) edging-distance ) turn left

9. Basic Navigation Correct : if( sonar 11 edging-distance ) turn left if( sonar 6 edging- distance )turn right

10. Landmark Detection 4 types of landmarks: Right wall (RW); consistent right wall and consistent direction. Right wall (RW); consistent right wall and consistent direction. Left wall (LW); consistent left wall and consistent direction. Left wall (LW); consistent left wall and consistent direction. Corridor (C); consistent left and right walls and consistent direction. Corridor (C); consistent left and right walls and consistent direction. Irregular (I); inconsistent walls and inconsistent direction. Irregular (I); inconsistent walls and inconsistent direction. How does Toto identify landmarks? ConfidenceCounter!

11. Landmark Detection After a time interval, sonar and compass readings are taken. Confidence Counter increments when sonar and compass readings are the same as last time interval. Predetermined threshold identifies how many time intervals are needed to justify a landmark

12. Landmark Detection

13. Mapping Landmarks After discovered, landmarks are stored in Toto’s internal map. Landmark nodes store information discovered through sensors and compass ( see next slide ). Nodes communicate with neighbors.

14. Mapping Landmarks Landmark node has a set where T is { LW, RW, C, I }; qualitative landmark type. C is [ 0 … 15 ]; averaged compass bearing. L is [ 1 … 127 ]; rough estimate of landmark’s length. P = ( x, y ) -128 <= x, y <= 127; coarse position estimate. Length is obtained through timer (could be confidence counter). Position is obtained through length and compass.

15. Mapping Landmarks Example:

16. Path Planning Use the map to go to a goal. This is done by activating one of Toto’s previously visited landmarks as a goal.

17. Path Planning Goal sends signal to neighbor nodes. Eventually, all nodes know where goal is. Greedy algorithm ensures Toto will take shortest path to goal. Toto can go to goal starting from any landmark. Toto can adapt to a changing environment.

18. Review Explores Finds landmarks Stores landmarks in map Goes to goal

19. Conclusion Toto explores, maps, plans, and finds goals without Deliberative or Hybrid process. Toto “extends the repertoire of integrated reactive systems to tasks requiring spatial modeling and user interaction.” Toto can adapt to a dynamic environment.

20. Discussion Is linear-time path planning reactive? Can a Behavior-Based system do anything a hybrid system can do? How is the open-space behavior triggered?